How to Interpret Machine Learning Models using SHAP in Python | Python Project Tutorial | Part 2

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In this tutorial, we will guide through theory about what is Shapely Additive Explanation (SHAP). SHAP is a neural network used for interpretation of predictions made by complex machine learning models. It is a neural system which opens up about the black box which occurs in machine learning.

Here we will demonstrate how to apply these techniques in Python on a real-world data science problem using an example of Melbourne Housing Market Dataset.
In this project-based video, you will get insights about how to install SHAP, SHAP graphs and SHAP values using demos.

This video is for a beginner in data science to an advanced level programmer

The Code:

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#SHAP #Python #Programming #Machine Learning
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Along with the video, you will learn:

00:00 - Introduction
00:16 - Demo 2: SHAP Values on Classification Problem
10:29 - Demo 3: SHAP Values on Deep Learning Model

Let us know if you have any questions in the comments.
15.27 - Thank you and Subscribe

[pra]

I can apply shap library and interpret the chart but what is final report out if it ??? Like what management / user expect from it ??? I can't see this chart to non-technical person . is there any report can be generated to draw any conclusion